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Zhang R, Murray SB, Duval CJ, Wang DJ, Jann K. Functional Connectivity and Complexity Analyses of Resting-State fMRI in Pre-Adolescents with ADHD. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.17.23294136. [PMID: 37662367 PMCID: PMC10473793 DOI: 10.1101/2023.08.17.23294136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Attention deficit hyperactivity disorder (ADHD) has been characterized by impairments among distributed functional brain networks, e.g., the frontoparietal network (FPN), default mode network (DMN), and reward and motivation-related circuits (RMN). In the current study, we evaluated the complexity and functional connectivity (FC) of resting state fMRI (rsfMRI) in pre-adolescents with ADHD for pathology-relevant networks. We leveraged data from the Adolescent Brain and Cognitive Development (ABCD) Study. The final study sample included 63 children with ADHD and 92 healthy control children matched on age, sex, and pubertal development status. For selected regions in relevant networks, ANCOVA compared multiscale entropy (MSE) and FC between the groups. Finally, differences in the association between MSE and FC were evaluated. We found significantly reduced MSE along with increased FC within the FPN of pre-adolescents with ADHD compared to matched healthy controls. Significant partial correlations between MSE and FC emerged in fewer regions in the participants with ADHD than in the controls. The observation of reduced entropy is consistent with existing literature using rsfMRI and other neuroimaging modalities. The current findings of complexity and FC in ADHD support hypotheses of altered function of inhibitory control networks in ADHD.
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Affiliation(s)
- Ru Zhang
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Stuart B. Murray
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Christina J. Duval
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Danny J.J. Wang
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Kay Jann
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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Angulo-Ruiz BY, Muñoz V, Rodríguez-Martínez EI, Cabello-Navarro C, Gómez CM. Multiscale entropy of ADHD children during resting state condition. Cogn Neurodyn 2023; 17:869-891. [PMID: 37522046 PMCID: PMC10374506 DOI: 10.1007/s11571-022-09869-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/18/2022] [Accepted: 08/05/2022] [Indexed: 11/28/2022] Open
Abstract
This present study aims to investigate neural mechanisms underlying ADHD compared to healthy children through the analysis of the complexity and the variability of the EEG brain signal using multiscale entropy (MSE), EEG signal standard deviation (SDs), as well as the mean, standard deviation (SDp) and coefficient of variation (CV) of absolute spectral power (PSD). For this purpose, a sample of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) between 6 and 17 years old were selected based on the number of trials and diagnostic agreement, 32 for the open-eyes (OE) experimental condition and 25 children for the close-eyes (CE) experimental condition. Healthy control subjects were age- and gender-matched with the ADHD group. The MSE and SDs of resting-state EEG activity were calculated on 34 time scales using a coarse-grained procedure. In addition, the PSD was averaged in delta, theta, alpha, and beta frequency bands, and its mean, SDp, and CV were calculated. The results show that the MSE changes with age during development, increases as the number of scales increases and has a higher amplitude in controls than in ADHD. The absolute PSD results show CV differences between subjects in low and beta frequency bands, with higher variability values in the ADHD group. All these results suggest an increased EEG variability and reduced complexity in ADHD compared to controls. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09869-0.
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Affiliation(s)
- Brenda Y. Angulo-Ruiz
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Vanesa Muñoz
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Elena I. Rodríguez-Martínez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Celia Cabello-Navarro
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Carlos M. Gómez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
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Kamal SM, Babini MH, Tee R, Krejcar O, Namazi H. Decoding the correlation between heart activation and walking path by information-based analysis. Technol Health Care 2023; 31:205-215. [PMID: 35848002 DOI: 10.3233/thc-220191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROND One of the important areas of heart research is to analyze heart rate variability during (HRV) walking. OBJECTIVE In this research, we investigated the correction between heart activation and the variations of walking paths. METHOD We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation. RESULTS According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series. CONCLUSION The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.
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Affiliation(s)
| | | | - Rui Tee
- School of Pharmacy, Monash University, Selangor, Malaysia
| | - Ondrej Krejcar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic.,Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Hamidreza Namazi
- School of Engineering, Monash University, Selangor, Malaysia.,Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
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Ahamed MRA, Babini MH, Namazi H. Analysis of the information transfer between brains during a conversation. Technol Health Care 2021; 29:283-293. [DOI: 10.3233/thc-202366] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND: The interaction between people is one of the usual daily activities. For this purpose, people mainly connect with others, using their voice. Voices act as the auditory stimuli on the brain during a conversation. OBJECTIVE: In this research, we analyze the relationship between the brains’ activities of subjects during a conversation. METHODS: Since human voice transfers information from one subject to another, we used information theory for our analysis. We investigated the alterations of Shannon entropy of electroencephalography (EEG) signals for subjects during a conversation. RESULTS: The results demonstrated that the alterations in the information contents of the EEG signals for the listeners and speakers are correlated. Therefore, we concluded that the brains’ activities of both subjects are linked. CONCLUSION: Our results can be expanded to analyze the coupling among other physiological signals of subjects (such as heart rate) during the conversation.
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Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study. Phys Eng Sci Med 2020; 43:577-592. [DOI: 10.1007/s13246-020-00858-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/10/2020] [Indexed: 12/16/2022]
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Pulini AA, Kerr WT, Loo SK, Lenartowicz A. Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:108-120. [PMID: 30064848 PMCID: PMC6310118 DOI: 10.1016/j.bpsc.2018.06.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 06/15/2018] [Accepted: 06/18/2018] [Indexed: 11/21/2022]
Abstract
BACKGROUND Motivated by an inconsistency between reports of high diagnosis-classification accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this study assessed classification accuracy in studies of ADHD as a function of methodological factors that can bias results. We hypothesized that high classification results in ADHD diagnosis are inflated by methodological factors. METHODS We reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias. RESULTS Circular analysis was detected in 15.9% of ADHD classification studies, lack of independent test set was noted in 13%, and insufficient methodological detail to establish its presence was noted in another 11.6%. Accuracy of classification ranged from 60% to 80% in the 59.4% of reviewed studies that met criteria for independence of feature selection, model construction, and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes. CONCLUSIONS High classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD.
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Affiliation(s)
| | - Wesley T Kerr
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles; Department of Biomathematics, University of California, Los Angeles, Los Angeles; Department of Internal Medicine, Eisenhower Medical Center, Rancho Mirage, California
| | - Sandra K Loo
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles.
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Hager B, Yang AC, Brady R, Meda S, Clementz B, Pearlson GD, Sweeney JA, Tamminga C, Keshavan M. Neural complexity as a potential translational biomarker for psychosis. J Affect Disord 2017; 216:89-99. [PMID: 27814962 PMCID: PMC5406267 DOI: 10.1016/j.jad.2016.10.016] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 10/12/2016] [Accepted: 10/18/2016] [Indexed: 12/13/2022]
Abstract
BACKGROUND The adaptability of the human brain to the constantly changing environment is reduced in patients with psychotic disorders, leading to impaired cognitive functions. Brain signal complexity, which may reflect adaptability, can be readily quantified via resting-state functional magnetic resonance imaging (fMRI) signals. We hypothesized that resting-state brain signal complexity is altered in psychotic disorders, and is correlated with cognitive impairment. METHODS We assessed 156 healthy controls (HC) and 330 probands, including 125 patients with psychotic bipolar disorder (BP), 107 patients with schizophrenia (SZ), 98 patients with schizoaffective disorder (SAD) and 230 of their unaffected first-degree relatives (76 BPR, 79 SADR, and 75 SZR) from four sites of the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Using multi-scale entropy analysis, we determined whether patients and/or relatives had pathologic differences in complexity of resting-state fMRI signals toward regularity (reduced entropy in all time scales), or toward uncorrelated randomness (increased entropy in fine time scales that decays as the time scale increases) and how these complexity differences might be associated with cognitive impairment. RESULTS Compared to HC subjects, proband groups showed either decreased complexity toward regularity or toward randomness. SZ probands showed decreased complexity toward regular signal in hypothalamus, and BP probands in left inferior occipital, right precentral and left superior parietal regions, whereas no brain region with decreased complexity toward regularity was found in SAD probands. All proband groups showed significantly increased brain signal randomness in dorsal and ventral prefrontal cortex (PFC), and unaffected relatives showed no complexity differences in PFC regions. SZ had the largest area of involvement in both dorsal and ventral PFC. BP and SAD probands shared increased brain signal randomness in ventral medial PFC, BP and SZ probands shared increased brain signal randomness in ventral lateral PFC, whereas SAD and SZ probands shared increased brain signal randomness in dorsal medial PFC. Only SZ showed increased brain signal randomness in dorsal lateral PFC. The increased brain signal randomness in dorsal or ventral PFC was weakly associated with reduced cognitive performance in psychotic probands. CONCLUSION These observations support the loss of brain complexity hypothesis in psychotic probands. Furthermore, we found significant differences as well as overlaps of pathologic brain signal complexity between psychotic probands by DSM diagnoses, thus suggesting a biological approach to categorizing psychosis based on functional neuroimaging data.
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Affiliation(s)
- Brandon Hager
- Massachusetts Mental Health Center, Boston, MA, USA; Division of Public Psychiatry, Beth Israel Deaconess Medical Center/Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Albert C Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Taipei Veterans General Hospital/School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Roscoe Brady
- Massachusetts Mental Health Center, Boston, MA, USA; Division of Public Psychiatry, Beth Israel Deaconess Medical Center/Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Shashwath Meda
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, and the Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Brett Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, and the Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Carol Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, USA
| | - Matcheri Keshavan
- Massachusetts Mental Health Center, Boston, MA, USA; Division of Public Psychiatry, Beth Israel Deaconess Medical Center/Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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McDonough IM, Nashiro K. Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project. Front Hum Neurosci 2014; 8:409. [PMID: 24959130 PMCID: PMC4051265 DOI: 10.3389/fnhum.2014.00409] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2014] [Accepted: 05/22/2014] [Indexed: 12/01/2022] Open
Abstract
An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity.
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Affiliation(s)
- Ian M McDonough
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas , Dallas, TX, USA
| | - Kaoru Nashiro
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas , Dallas, TX, USA
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